artificial neural networks fuzzy logic[1]
TRANSCRIPT
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 1/9
http://www.fullinterview.com
http://www.1000projects.com
*AUTOMATED AUTOMOBILES*
ABSTRACT
Automobiles have become an integrated part of our daily life. The
development of technology has improved the automobile industry in
both cost & efficiency. Still, accidents prove as challenge to
technology. Highway accident news are frequently found in the
newspapers. The automobile speed has increased with development
in technology through years and the complexity of the accidents has
also increased. Higher speeds the accidents prove to be more fatal.
Man is intelligent with reasoning power and can respond to any
critical situation. But under stress and tension he falls as a prey to
accidents. The manual control of speed & braking of a car fails
during anxiety. Thus automated speed control & braking system is
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 2/9
http://www.fullinterview.com
http://www.1000projects.com
required to prevent accidents. This automation is possible only with
the help of Artificial Intelligence (Fuzzy Logic).
In this paper, Fuzzy Logic control system is used to control the
speed of the car based on the obstacle sensed. The obstacle sensor
unit senses the presence of the obstacle. The sensing distance
depends upon the speed of the car. Within this distance, the angle of
the obstacle is sensed and the speed is controlled according to the
angle subtended by the obstacle. If the obstacle cannot be crossed bythe car, then the brakes are applied and the car comes to rest before
colliding with the obstacle. Thus, this automated fuzzy control unit
can provide an accident free journey.
INTRODUCTION
Fuzzy logic is best suited for control
applications, such as temperature control,
traffic control or process control. Fuzzy
logic seems to be most successful in two
kinds of situations:
i) Very complex models where
understanding is strictly limited, in fact,
quite judgmental.
ii) Processes where human reasoning,
human perception , or human decision
making are inextricably involved.
Our understanding of physical processes
is based largely on imprecise human
reasoning. This imprecision (when
compared to the precise quantities
required by computers) is nonetheless a
form of information that can be quite
useful to humans. The ability to embed
such reasoning and complex problem is
the criterion by which the efficacy of
fuzzy logic is judged.
Undoubtedly this ability cannot solve
problems that require precision - problems such as shooting precision laser
beam over tens of kilometers in space;
milling machine components to
accuracies of parts per billion; or focusing
a microscopic electron beam on a
specimen of size of a nanometer. The
impact of fuzzy logic in these areas might
be years away if ever. But not many
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 3/9
http://www.fullinterview.com
http://www.1000projects.com
human problems require such precision -
problems such as parking a car,
navigating a car among others on a free
way, washing clothes, controlling traffic
at intersections & so on. Fuzzy logic is
best suited for these problems which do
not require high degree of precision.
Fuzzy Vs. Probability:
Fuzziness describes the ambiguity of an
event, whereas randomness (probability)
describes the uncertainty in the
occurrence of the event.
An example involves a personal choice.
Suppose you are seated at a table on
which rest two glasses of liquid. The
liquid in the first glass is described to you
as having a 95 percent change of being
healthful and good. The liquid in the
second glass is described as having a 0.95
membership in the class of “healthful &
good” liquids. Which glass would you
select, keeping in mind first glass has a 5
percent change of being filled with non-
healthful liquids including poisons.
What philosophical distinction can be
made regarding these two forms of
information? Suppose we are allowed to
test the liquids in the glasses. The prior
probability of 0.95 in each case becomes
a posterior probability of 1.0 or 0; i.e.,the liquid is either benign or not.
However, the membership value of 0.95,
which measures the drinkability of the
liquid is “healthful & good”, remains 0.95
after measuring & testing. These
examples illustrate very clearly the
difference in the information content
between change & ambiguous events.
Complexity of a System vs.
Precision in the model of the
System:
For systems with little complexity, hence
little uncertainty, closed-form
mathematical expressions provide precise
descriptions of the systems. For systems
that are a little more complex, but for
which significant data exist, model-free
methods, such as artificial neural
networks, provide a powerful and robust
means to reduce some uncertainty through
learning, based on patterns in the
available data. Finally, for the most
complex systems where few numerical
data exist and where only ambiguous or
imprecise information may be available.
Fuzzy reasoning provides a way to
understand system behaviour by allowing
us to interpolate approximately between
observed input and output situations.
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 4/9
http://www.fullinterview.com
http://www.1000projects.com
Fuzzy Set vs. Crisp Set:
A classical set is defined by crisp
boundaries; i.e., there is no uncertainty in
the prescription or location of the
boundaries of the set. A fuzzy set, on the
other hand, is prescribed by vague or
ambiguous properties; hence its
boundaries are ambiguous.
If complete membership in a set is
represented by the number 1, and no
membership is represented by 0, then
point C must have some intermediate
value of membership (partial membership
in fuzzy set ~A
) on the interval [0, 1].
Presumably the membership of point C in
~A approaches a value of 1 as it moves
closer to the control (unshaded) region of
~A , and membership of point C in
~A
approaches a value of 0 as it moves closer
to leaving the boundary region of ~A .
Membership Function:
Membership function characterize the
fuzziness in a fuzzy set, whether the
elements in the set are discrete or
continuous - in a graphical form for
eventual use in the mathematical
formalisms of fuzzy set theory. Just as
there is infinite number of ways to
characterize fuzziness, there are an
infinite number of ways to graphically
depict the membership functions that
describe fuzziness.
Features of membership function:
The core of a membership function for
some fuzzy set~A is defined as that
region of the universe that is
characterized by complete and full
membership in the set~A , i.e., the core
comprises those elements x of the
universe such that µ ~A
(x) = 1.
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 5/9
http://www.fullinterview.com
http://www.1000projects.com
The support of a membership function for
some fuzzy set~A is defined as the
region of the universe that is
characterized by non zero membership in
the set A. that is, the support comprises
those elements x of the universe such that
µ A(x) > 0.
The boundaries of a membership function
for some fuzzy set~A are defined as the
region of the universe containing
elements that have non zero membership,
but not complete membership. That is,
the boundaries comprise these elements x
of the universe such that 0 < µ A (x) < 1.
Fuzzification:
Fuzzification is the process of making a
crisp quantity fuzzy. We do this by
simply recognizing that many of the
quantities that we consider to be crisp &
deterministic are actually not
deterministic at all. They carry
considerable uncertainty. If the form of uncertainty happens to arise because of
imprecision, ambiguity, or fuzzy and can
be represented by a membership function.
In this paper institution method is used for
fozzification of the input variables, as it is
very simple
Defuzzification:
Defuzzification is the conversion of a
fuzzy quantity to a precise quantity, just
as fuzzification is the conversion of
precise quantity to a fuzzy quantity.
Some of the defuzzification techniques
are:
1. Max - Membership Principle:
Also known as the height method, this
scheme is limited to peaked output
junctions. This method is given by the
algebraic expression
µ c (Z*) ≥ µ ~
C (Z) for all z ∈ Z
2. Centroid Method:
This procedure (also called center of area,
center of gravity) is the most prevalent &
physically appealing of all the
defuzzification methods; it is given by
the algebraic expression:
Z* =
∫ ∫
(Z)dzCμ
(Z).zdzCμ
~
~
3 . Weighted Average Method :
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 6/9
http://www.fullinterview.com
http://www.1000projects.com
This method is only valid for symmetrical
output membership function. It is given
by the algebraic expression:
Z* =∑
∑)z(cμ
z).z(cμ
~
~, where Σ
denotes an algebraic sum.
4 . Means-Max Membership:
This method (also called middle-of-
maxima) is closely related to the first
method, except that the locations of the
maximum membership can be non-unique
(i.e., the maximum membership can be a
plateau rather than a single point. This
method is given by the expression:
Z*
= 2
ba +
, where a & b are
shown in the figure.
In this paper, centroid method is used for
defuzzification if the output variables.
FUZZY LOGIC CONTROL
SYSTEM:
Obstacle Sensor Unit:
The car consists of a sensor in the front
panel to sense the presence of the
obstacle.
Sensing Distance:
The sensing distance depends upon the
speed of the car. As the speed increases
the sensing distance also gets increased,
the obstracle can be sensed at a large
distance for higher speed and the speed
can be controlled by gradual anti skid
braking system.
The speed of the car is taken as the input
and the distance sensed by the sensor is
controlled.
Input Membership Function:
Output Membership Function:
The input & output are properly related
by the If i/p then o/p rules.
The defuzzified values are obtained and
the variation of speed with sensing
distance is plotted as a surface graph
using matlab
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 7/9
http://www.fullinterview.com
http://www.1000projects.com
From the graph it is clear that the sensing
distance almost varies linearly with speed.
And the curve is not very smooth because
we deal with fuzzy values.
Speed Control:
The speed of the car is controlled
according to the angle subtended by the
obstacle. The angle subtended by the
obstacle is sensed at every instant.
Obstacles which the car can overcome:
At any instant, if the obstacle subtends an
angle less than 60°, then the car can
overcome the obstacles and the speed of
the car reduces according to the angle.
1. Speed breaker
2. Fly over
Obstacles which the car cannot overcome:
At any instant, if the angle subtended bythe obstacle is greater than 60°, then the
car comes to rest before colliding with the
obstacle as the car cannot overcome the
obstacle.
E.g. 1. Vehicles
The angle is taken as the i/p & the
o/p speed is controlled.
Input - Membership Function:
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 8/9
http://www.fullinterview.com
http://www.1000projects.com
Output - Membership Function:
The input & the output functionsare related by the If i/p then o/p rules.
Using matlab the surface graph relating
the speed and angle is obtained.
From the graph it is clear that the
speed becomes zero when the angle of the
obstacle is greater than 60°.
The rules are applicable not only for
obstacles that have elevation but also
depression like a small pit, subway, etc.
Rear Sensing:
This fuzzy control can be extended to rear
sensing by placing a sensor at the back
side of the car, and can be used to control
the motion of the car when the wheals
http://www.fullinterview.com
http://www.1000projects.com
8/7/2019 Artificial Neural Networks Fuzzy Logic[1]
http://slidepdf.com/reader/full/artificial-neural-networks-fuzzy-logic1 9/9
http://www.fullinterview.com
http://www.1000projects.com
rotate in the opposite direction or when
the car is in rear gear.
Simulation Results:
Some of the sample readings
obtained from the matlab simulations are
shown:
Conclusion:
In this world of stress and tension where the drivers
concentration is distracted in many ways, an automated
accident prevention system is necessary to prevent accidents.
The fuzzy logic control system can relieve the driver from
tension and can prevent accidents. This fuzzy control unit
when fitted in all the cars can result in an accident free
world.
http://www.fullinterview.com
http://www.1000projects.com